Search Results for author:
Found 7 papers, 0 papers with code
How catastrophic can catastrophic forgetting be in linear regression?
In specific settings, we highlight differences between forgetting and convergence to the offline solution as studied in those areas.
Tight Complexity Bounds for Optimizing Composite Objectives
We provide tight upper and lower bounds on the complexity of minimizing the average of m convex functions using gradient and prox oracles of the component functions.
Normalized Spectral Map Synchronization
The algorithmic advancement of synchronizing maps is important in order to solve a wide range of practice problems with possible large-scale dataset.
Beating SGD: Learning SVMs in Sublinear Time
We present an optimization approach for linear SVMs based on a stochastic primal-dual approach, where the primal step is akin to an importance-weighted SGD, and the dual step is a stochastic update on the importance weights.
On the Universality of Online Mirror Descent
We show that for a general class of convex online learning problems, Mirror Descent can always achieve a (nearly) optimal regret guarantee.
Learning with the weighted trace-norm under arbitrary sampling distributions
We provide rigorous guarantees on learning with the weighted trace-norm under arbitrary sampling distributions.
Better Mini-Batch Algorithms via Accelerated Gradient Methods
Mini-batch algorithms have recently received significant attention as a way to speed-up stochastic convex optimization problems.
